Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as low-cost alternatives using frontal body images and basic anthropometric data. The dataset consists of 535 samples: 253 cases with recorded anthropometric measurements (weight, height, neck, ankle, and wrist) and 282 images obtained via web scraping from Reddit posts with self-reported body fat percentages, including some reported as DEXA-derived by the original posters. Because no public datasets exist for computer-vision-based body fat estimation, this dataset was compiled specifically for this study. Two approaches were developed: (1) ResNet-based image models and (2) regression models using anthropometric measurements. A multimodal fusion framework is also outlined for future expansion once paired datasets become available. The image-based model achieved a Root Mean Square Error (RMSE) of 4.44% and a Coefficient of Determination (R^2) of 0.807. These findings demonstrate that AI-assisted models can offer accessible and low-cost body fat estimates, supporting future consumer applications in health and fitness.

Predicting an individual's aging trajectory is a central challenge in preventative medicine and bioinformatics. While machine learning models can predict chronological age from biomarkers, they often fail to capture the dynamic, longitudinal nature of the aging process. In this work, we developed and validated a machine learning pipeline to predict age using a longitudinal cohort with data from two distinct time periods (2019-2020 and 2021-2022). We demonstrate that a model using only static, cross-sectional biomarkers has limited predictive power when generalizing to future time points. However, by engineering novel features that explicitly capture the rate of change (slope) of key biomarkers over time, we significantly improved model performance. Our final LightGBM model, trained on the initial wave of data, successfully predicted age in the subsequent wave with high accuracy ($R^2 = 0.515$ for males, $R^2 = 0.498$ for females), significantly outperforming both traditional linear models and other tree-based ensembles. SHAP analysis of our successful model revealed that the engineered slope features were among the most important predictors, highlighting that an individual's health trajectory, not just their static health snapshot, is a key determinant of biological age. Our framework paves the way for clinical tools that dynamically track patient health trajectories, enabling early intervention and personalized prevention strategies for age-related diseases.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. UK Biobank (UKB) has recruited more than half a million volunteers from the United Kingdom (UK), collecting health-related information on genetics, lifestyle, blood biochemistry, and more. Ongoing medical imaging of 100,000 participants, with 70,000 follow-up sessions, will furthermore yield up to 170,000 MRIs, enabling image analysis of body composition, organs, and muscle. This work presents an experimental inference engine for automated analysis of 1.5T UKB neck-to-knee body MRI.

CES is back in Las Vegas this year, and with that comes a slew of new product launches. The consumer technology event now has its own digital health track with a number of new companies and established names presenting and introducing new products. Read on below for MobiHealthNews' list of CES 2022's product announcements (and check back throughout the week as the reveals keep rolling in). Omron Healthcare announced a new remote patient monitoring tool and connected blood pressure monitors. Called VitalSight, the tool is designed to help individuals manage hypertension by sharing data to boost engagement and treatment.

It is well-established that patients with the highest proportion of visceral fat area are more likely to experience a heart attack or other cardiovascular event. While abdominal CT scans can provide a more granular look at body composition when routinely performed, ascertaining risk levels based on fat area is rarely done in clinical practice. Manually obtaining measurements can be time intensive and costly, yet a single axial CT slice of the abdomen can visualize the volume of subcutaneous and visceral fat area as well as skeletal muscle area needed to predict the risk of major cardiovascular (CV) events. Used in combination with artificial intelligence (AI), CT imaging has the potential to offer an improved way of predicting adverse CV according to emerging research. As part of a retrospective study including over 23,000 patients, a team of researchers at Brigham and Women's Hospital in Boston analyzed over 33,000 CT scans performed.

Mars pictured in natural color taken by the Rosetta spacecraft's Optical, Spectroscopic, and ... [ ] Infrared Remote Imaging System (OSIRIS). Humans have evolved over millions of years to live on Earth. Now humans are planning long duration space missions that will require them to live in space for extended periods of time. NASA plans to send humans to an asteroid by 2025 and to Mars in the 2030s. NASA's Journey to Mars, the longest manned space mission ever, will require humans to live in space for more than three years.